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   "source": [
    "# Data (pre) processing using `DataTransformer` and `Pipeline`\n",
    "\n",
    "In this notebook, we will demonstrate how to perform some common preprocessing tasks using `darts`\n",
    "\n",
    "As a toy example, we will use the [Monthly Milk Production dataset](https://www.kaggle.com/tejasgosavi/monthlymilkproductionpounds). "
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "## The `DataTransformer` abstraction\n",
    "\n",
    "`DataTransformer` aims to provide a unified way of dealing with transformations of `TimeSeries`:\n",
    "- `transform()` is implemented by all transformers. This method takes in a `TimeSeries`, transforms it and returns it as a new `TimeSeries`\n",
    "- `inverse_transform()` is implemented by transformers for which an inverse transformation function exists. It works in a similar way as `transform()`\n",
    "- `fit()` allows transformers to extract some information from the time series first before calling `transform()` or `inverse_transform()`\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "## Setting up the example"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fix python path if working locally\n",
    "from utils import fix_pythonpath_if_working_locally\n",
    "fix_pythonpath_if_working_locally()\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from darts import TimeSeries\n",
    "from darts.models import ExponentialSmoothing\n",
    "from darts.dataprocessing.transformers import Scaler, MissingValuesFiller, Mapper, InvertibleMapper\n",
    "from darts.dataprocessing import Pipeline\n",
    "from darts.metrics import mape\n",
    "from darts.utils.statistics import check_seasonality, plot_acf, plot_residuals_analysis\n",
    "from darts.utils.timeseries_generation import linear_timeseries\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import logging\n",
    "logging.disable(logging.CRITICAL)"
   ]
  },
  {
   "source": [
    "### Reading the data and creating a time series"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "            Pounds per cow\nMonth                     \n1962-01-01             589\n1962-02-01             561\n1962-03-01             640\n1962-04-01             656\n1962-05-01             727\n...                    ...\n1975-08-01             858\n1975-09-01             817\n1975-10-01             827\n1975-11-01             797\n1975-12-01             843\n\n[168 rows x 1 columns]\nFreq: MS\n"
     ]
    },
    {
     "output_type": "display_data",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "df = pd.read_csv('monthly-milk.csv')\n",
    "series = TimeSeries.from_dataframe(df, 'Month', ['Pounds per cow'])\n",
    "\n",
    "print(series)\n",
    "series.plot()"
   ]
  },
  {
   "source": [
    "## Using a transformer: Rescaling a time series using `Scaler`.\n",
    "\n",
    "Some applications may require your datapoints to be between 0 and 1 (e.g. to feed a time series to a Neural Network based forecasting model). This is easily achieved using the default `Scaler`, which is a wrapper around `sklearn.preprocessing.MinMaxScaler(feature_range=(0, 1))`."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "                   0\nMonth               \n1962-01-01  0.086538\n1962-02-01  0.019231\n1962-03-01  0.209135\n1962-04-01  0.247596\n1962-05-01  0.418269\n...              ...\n1975-08-01  0.733173\n1975-09-01  0.634615\n1975-10-01  0.658654\n1975-11-01  0.586538\n1975-12-01  0.697115\n\n[168 rows x 1 columns]\nFreq: MS\n"
     ]
    }
   ],
   "source": [
    "scaler = Scaler()\n",
    "rescaled = scaler.fit_transform(series)\n",
    "print(rescaled)"
   ]
  },
  {
   "source": [
    "This scaling can easily be inverted too, by calling `inverse_transform()`"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "                0\nMonth            \n1962-01-01  589.0\n1962-02-01  561.0\n1962-03-01  640.0\n1962-04-01  656.0\n1962-05-01  727.0\n...           ...\n1975-08-01  858.0\n1975-09-01  817.0\n1975-10-01  827.0\n1975-11-01  797.0\n1975-12-01  843.0\n\n[168 rows x 1 columns]\nFreq: MS\n"
     ]
    }
   ],
   "source": [
    "back = scaler.inverse_transform(rescaled)\n",
    "print(back)"
   ]
  },
  {
   "source": [
    "Note that the `Scaler` also allows to specify other scalers in its constructor, as long as they implement `fit()`, `transform()` and `inverse_transform()` methods on `TimeSeries` (typically scalers from `scikit-learn`)"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "## Another example : `MissingValuesFiller`\n",
    "\n",
    "Let's look at handling missing values in a dataset."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "df = pd.read_csv('monthly-milk-incomplete.csv')\n",
    "incomplete_series = TimeSeries.from_dataframe(df, 'Month', ['Pounds per cow'])\n",
    "incomplete_series.plot()\n"
   ]
  },
  {
   "source": [
    "filler = MissingValuesFiller()\n",
    "filled = filler.transform(incomplete_series)\n",
    "\n",
    "filled.plot()"
   ],
   "cell_type": "code",
   "metadata": {},
   "execution_count": 7,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ]
  },
  {
   "source": [
    "Since `MissingValuesFiller` wraps around `pd.interpolate` by default, we can also provide arguments to the `pd.interpolate()` function when calling `transform()`"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "filled = filler.transform(incomplete_series, method='quadratic')\n",
    "filled.plot()"
   ]
  },
  {
   "source": [
    "## `Mapper` and `InvertibleMapper`: A special kind of transformers\n",
    "\n",
    "Sometimes you may want to perform a simple `map()` function on the data. This can also be done using data transformers. `Mapper` takes in a function and applies it to the data elementwise when calling `transform()`.\n",
    "\n",
    "`InvertibleMapper` also allows to specify an inverse function at creation (if there is one) and provides the `inverse_transform()` method."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x13ffd7f70>"
      ]
     },
     "metadata": {},
     "execution_count": 9
    },
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "lin_series = linear_timeseries(start_value=0, end_value=2, length=10)\n",
    "\n",
    "squarer = Mapper(lambda x: x**2)\n",
    "squared = squarer.transform(lin_series)\n",
    "\n",
    "lin_series.plot(label=\"original\")\n",
    "squared.plot(label=\"squared\")\n",
    "plt.legend()"
   ]
  },
  {
   "source": [
    "### More complex (and useful) transformations\n",
    "\n",
    "In the Monthly Milk Production dataset used earlier, some of the difference between the months comes from the fact that some months contain more days than others, resulting in a larger production of milk during those months. This makes the time series more complex, and thus harder to predict. "
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1400f65e0>"
      ]
     },
     "metadata": {},
     "execution_count": 10
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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5//77+cpX1Fb6Bw4cYM2aNVx11VX853/+Jz/72c8ANVwzPDxMVlYWeXl59Pb2sm3bNrZs2YLb7WZsbIzrrruOjRs3UltbC0BOTg4jIyNxX4NEciGiKArOITjoUb/Xe8Kacxh8kw6OHj3Ktdd7aOlOQa+KVM2T72xtoP2E+rReWgi9Ln2qXuXCawzce++97Nu3j/r6ev7lX/6FX/3qV2H3u++++7Db7dTX17N8+XIefPBBAP793/+dgYEBVq5cyerVq3nllVdYvXo1a9euZenSpXz84x9n48aNAIyMjHD99ddTX1/Ppk2b+MlPfgLAxz72MX74wx+ydu1aufAqOa+46aabps071pORMfD6YGBErUrVG+cQeMbUBUGDx6FrsZJzGDLSYNDZzZEjR1AU5eL15M8WoXNln3nmmVnb77333mk/FxcX8+STT87aLzs7O+yN4dFHHw173Lfeemvaz52dnWzcuFGmUErOS/bs2YPFYklK0oDmDYMaSik362u/b9CPf1K9e4wNNTNk1K+nvVbt6mxQ1/i6urooLVzAoWZ97EtPXiKRJB2fz8fAwMCsWQt6ocW1U42TNCThAbdv0A8eJ8uWLcPZfVh3T744D1wu1eiRI0coLRK6HUOKvEQiSToDAwMoisKhQ4eCM4/1RBP5QmNjYEaqvriGAa+La6+9lrYTb9M7oF+ufP+QQl6WN1hIeeTIEV3DNVLkJRJJ0unv7wdgdHSU5mad4hCh9gcV8I2C+23dM2x8PoXhMSN4nGzdupVBx1FdK1Kdw5CVNgmAxWLhyJEjWArUG8vkVOI3EinyEokk6Tidp3vnJiNk09U3CR4ng517ONIKHq9+3vygG/yKwOB3sXnzZkyKei09OrUDPtIKRVlqxtymTZtobGwMVr06BhK3L0VeIpEknf7+foQQLF++PCki39E7Dl4nE8638HihqU0/25rHnpflIz09nVVL1VXdXh0EuG9Q4cBxWGLpBFSRV2Py6nY9QjZS5CUSSdJxOp0UFhaydu3apIh8d98UeJwwfpK0FJ+ui69a5k5hjvp0sGxxJSYxposA79gHqSmwIPMkWVlZrFmzBofDgdE/gBD63EikyEskEkBtlGe325Niu7+/n6KiIurr65Mi8g6XD7xOsrIyKM0d5EirfuEazZMvKVAzzisqKjD5+3UJ12zfp7BxJbiHHRQVFbFs2TIAThw/QnGe9OQlEomOPProozz33HNJsd3f309xcTGrVq3i5MmTuN1ufe0PK6SIEZYsWUKq0q9viuMQpBrGKCpUewFXVlbin+imx5XYjURRFF6ywwdsAqfTSVFREWazmYKCgtMZNjrcSKTISyQSxsbGmJiYSFqhnSZi9fX1KIrC4cOHdbU/5DaSlTpBXV0dvoku3fPYU8QwhYXqamhlZSVT7ja6+hNLBT3VBa09cJXt9OcjhGDZsmXBxddup8yukUgkOqBlvxw5ciQp9jVPvqysjMLCQt1DNiMTqeRleqmrq2N8qFlnkVcw+gemiTxTPbR2TyZkd7sd8rNh3eLTIg9QV1fHqVOnKCvWZ6C3FHmJRBKstjx27FhSpps5nU6Ki4sRQrBo0SJOndI3mX3Mk0F+tp+6ujoGHEd0S28ENVyjeJzB1uCVlZXg6aE7QU9+17sKW9aC0SimiXxlZSXt7e1UmKGtN+HTlyIvkUhOe/JTU1PJKVYKLLyCKmJtbfrlOE55FDz+dIrz1RvIxFALvQOKbhWpzmHwTfQFPfm8vDzSDMP0DRkTstvrgqpA5/NQka+oqKC9vZ1Ki6DdkdAhACnyEokEVWRMJhMGgyEpIRstXAP6i7yW4mgpNFFXVweeHjxewYBOHbm7neAZ6wqKvBACS4Ef92Q6U574byQjY5CTqX6vpZiCKvJ9fX2UFkzRNwjjk4ndrKTISyQSXC4XxcXFLFy4UPfFV605WbI8eS3F0WpOx2KxkGFS1V2PuPzYhMLbRxU8zt1BEQawlqjplIlUpA6PQU6m2i9+picPYPKprY07EvTmpchLJJKgyCxbtkx3T35wcBC/3z/Nk+/s7MTn8+liXxP5igWZCCGoLVfdYz1EfncDTHkEDO6YJvK15RkJH2NkDHKz1BCZ2+2eJfL+8VYA2qTISySSRNHCBckQeS3eHyryPp+P7u5uXez3DfhB8VKxQJ3SVlNZgklM6FOsZFdYVjEBXuc0kV9coy7CJlKROjIGORmnPx9N5PPy8sjJycHpaCE/O/HFVynyEokEl8s1zZNXFP0qRrUOlKHhGkC3kE17zxh4XJSUqDcRq9VKKi5dPPnt+2BtjWpIy64BqKkuB09/3Hnsfr8SjMnPFHk4nWFTaSHhxVcp8hKJJBiuWb58OW63m46ODt1sa83JNJEsKioiPT1dN5Hv6BkDr5OSkhIAysvLwdNL70BiNyqtedjSUvWzyM/PD25Tc+W7aemaiMv2aOBtOZmn01dDRf50hg209cqFV4lEkiBauGbp0qWAvkVRTqeT/Px8TCZ1sVIIoevia1egOZkWDrJarXhG2xP25F/eB2mpavOwvLy84PmDJvK9nGgbi8v2SOBtuVnq52MwGMjLywtur6iooK2tjQqz9OQlEokOaJ58Tk4OCxYs4Pjx47rZDk2f1NBT5B0DXvA6g552eXk5ntF2OvsSW9jdblebh42O9E+Lx4N6I2GqneYuT1y2h0fVr1q4prCwEIPhtBwHPXmzkDF5iUSSOFpMHlSB6ezs1M22Vu0aip4i3z8E6cbRoEiqAtxDpyOxyt3GFrhkifrZzBT51NRUslMG6OqPryBK8+S1hdfQUA2cFvkKM7T3kdAaiRR5ieQiR1GUaSJvtVp1FfnQalcNPUV+0G0kK+10bFwV+V56B0RCdkfGIC9LhBV5gJLcCfrdmXHbhtPhmnAiPzQ0RHHOGKPjJFTYZZp/l9nYbDYj8CvACjQDdwIfAr4AjAO32e32DpvNthR4KHCce+x2+8vxn6pEIkkGQ0ND+Hy+oJBZrVbdY/LhPPn29vaEbXu9Cr0jRVRmnM5lzM3NJd04yNBoCh6vQoopPrEfDmS/NM4h8hUlCs0DmYyOK2RlxHaM4TEwGCAzfW6RBzB6O4E62nqhMDeuy4jbk/8Q0Gy3298HHAU+DHwR2AJ8DbgnsN93gL8HtgLfjPNYEokkiczM7jhTnrzL5Uq4r/y+YzDpy6C28HTDM7XtgIKCoG8wfttasZLT6ZyWPqlRW54KxLcwOjIG2RnquTY3N2OxWKZt10R+cqQFIRJbfI1X5BcCBwLfvwP8A3DEbrdP2e3214D6wLYyu91+3G63DwMum81WHMaWRCI5i8zM07ZarbqmUPb09IQVeSBhb367HTJED1WW6R0hyy2qAMdbEKUoah57mmmKN954g5UrV87aZ/nCHCB+kc/NhOPHj/PKK69wyy23TNuekZFBcXEx3V1tlBUnVhAVV7gGaET1zv8AfAD1ZjEcsl1bjQi9iQwBhUB/qCGbzXYnariHz33uc1x11VVxntL8eDweXT2UM408/7PP+X4N4c6/qakJgPHxcTo7O0lLS8PtdnP06FFycnISOp7dbufEiROsXLly2nGFUMMb77zzDrm50cchZp7/n18vJH18D+np6dNeNxcADjh03IklO/a+7+NT4PUt4M3XX2Z0dJTNmzfP+twK89LA42R/o2B5WXTH0M6/vTuLjNQMfvCDH1BTU8Py5ctn2S8tLeXw4cNY8qdoPDVFZ+fcgXmr1TrntnhF/jlgi81m2wEcBgaB0N+UlrsUenvNA2Zlrtrt9odQ4/YA+pXZhaGzszPih3GuI8//7HO+X0O48zcYDGRlZVFbWwvA6tWrAdWbTfRa7777brZs2cINN9wwa1tJSQljY2MxHSP0/EfHFfYd8zPV/DuuufeOaXaWLanl2d4hvKIIqzX2mLxjQAEUDth3sXXrVurr62fts3btWphso8u1EKs1uiCFdv6GFD95WT6eeuopvvWtbwXDM6HU1tYyODhIXWUqrtFUrNb4gvJxibzdbleALwHYbLZ7gd8D37PZbKmADdDGvnTbbLaFgAMotNvt/WHMSSSSs8jMhT9NLDs7O4ODpeOhsbGRZ599lm3btoXdnmiGzZ6D4PFCluctrrzysWnbrFYrwtNDjyt/jndHRst+eW33X3j8F18Nu09lZSVMvsaJtthvhCNjMDLUjc/n47bbbgu7T1VVFYcOHcJ2Gbx2MOZDBIk3u6YU+A2qp/6y3W7fZbPZfgbsBCYA7az/DXgUNXzz9fhPUyKRJIvQXuYA2dnZ5OXlJRyW+uEPf8jq1au55pprwm5PVOS32xVyDSe56qrLSE9Pn7atvLwc72gb7Y7FnI4eR49WrJRmnAr7FALqGobR20lrHG0HRsZgyNXOzTffPK3SNZTq6mr+/Oc/82Gz4NcJtDaI15PvAd4347UngSdnvNYIbI777CQSSdIJl8KnR4bNyy+/zBe+8IVg/H0mlZWVvPvuu3Hb37nfh7vzOW7+9M2ztlmtVpjcz4m2SSD2XHbNk//g1s1kZoZ/vxCCgsxRegZSYrY/PAqKdyTYbyccVVVVtLW1UW7209UvmPIopKbEHnqSxVASyXnCZz/7WV3bDWiEFkJp6CHyw8PDs+yGkmiufHvPBMpkF9ddd92sbeXl5TDZRmtPfK0NRsbVr3U1pRH3Ky2YYmAsO+aK1JExUDxDZGVlzblPdXU1Xq+XLGMfigIdfTEdIogUeYnkPGBqaooHHniAP/3pT7rbnhmugcRFXlEURkZGImbnaCLv98c3EHtoxMfihQvC5rCXlJRg9HbT7YrdywbV0xbKODnZGRH3qyoV+JTU4AjCaBkZB9/UYESRr6qqAsA/3gJAa09sx9CQIi+RnAdoueyHDh1Kiu1wnnwiufLj4+P4/f6I6ZGVlZVMTU3hcMRX6TPpS2XVsqqw2wwGA0XZY7gnU3GPxRczF353RBEGWFSl3gRizWMfHp1f5IuKisjMzKS/5yR52dAaZ668FHmJ5DwgmSIfLlxTXl6ekCc/MqLmdM/nyUN8w0M8XgVFpJOfO/eiqrVYDdXEW6yEb2RekV9RVwCKL6oRfYMjCqMTImjfM+GKaF8IQXV1Na2trVRZpCcvkVzQaCJ/+PDhuMMb4ZicnKSzs5OysrJpr1utVnp7e/F44mulG43Im81mUlJS4hJ5bWE0P3tuka+xqqGaeGakDo8pKJ7hORddg8eoroCpLk60z18Mdeu3Fb7zG/XzGBmDqfH+eW8iVVVVtLS0UJXA8BAp8hLJeYA2Qm9sbIzm5mbd7O7du5fJyUk2b56eBGe1WlEUhZ6e+NzH4WG1AD6SyBsMhuBwjFjRRL4gd+4EwaryElIYjMuTHx4FxRM5nAKBHjMTbTSenL8Hz74mONZhQlEUhsfAMx7ZkwdOe/KlMlwjkZx13n33XV297FC06UpGo1HXkM2OHTtYtmxZWE8eiDtkE40nD/Hnyg+Pql5tYd7cC6tWqxUx1RmXBzw44gXf/DH5iooKmGznZMdUxP0GRhS6ndDaa2J8Evx+wDcy75OC5slXWsSc4ZqvPBD5b06KvESiA2NjY1xyySX8+c9/Top9p9NJWVkZixYt0l3kr7zyylmvl5SUkJKSkrDIZ2dnR9wv3jTK/gE1PFKUnzrnPmpB1Cna4/CAXcO+qGLyGRkZZBj7aO+LLKWHAw9f3S4D/VomThT2q6uraWtro9Ks0OZQB4DPZF9TRBNS5CUSPejv78fn87F///6k2NcyYFauXKmbyLvdbt58882wIm8wGLBarbS2tsZle2RE9VKNxsjVpvF68g6XKvLFBXOLvNVqxT/eysnO2NcVhtx+8A3PK8IAJTljOIYjp1oeagYhQFEEB7WuyN757VdVVTExMUFu2gBTHugNM7e2ZZ6ImhR5iUQHtJ7sBw8m0GQkAlpP9pUrV+p2jN27d+Pz+diyZUvY7bW1tXHH/+fLkdeIV+Sdg5Pgn6Igb+5wR3l5OUy00dITewhtZFSJKpwCsKDQw+hUDhOTc4eFDjcrXBZoA3RAq2eLwpPXcuUVLVd+xlOJ16vMu+YgRV4i0QFN5BsaGubZMz40T37VqlU0NTUxNRU5BhwNO3bsYO3atWGnHgEsXLiQU6dOhVl/93QAACAASURBVN02HyMjI1G1EK6srMThcDA+Ph6TfdeQZ16RLCsrg6kOelymuCpSoxFhgPIS9SYSSWwPN4NtCZQW+jhwInAuUdi3WCykpaUx7DpJWiqc7ISPft3P1x9Rj9nlBO88Rb1S5CUSHdBE/vjx44yNjeluXxuht3LlSrxeL8eOHUvY5lzxeI3a2tqERD5aTx6IufBqYNg3bzglNTWVgowRPD5jzBOi3BMiqnAKQG2ZGpKKlP1yuAVW1AiqzN4QT37+hV2DwaA+7bS2UGmGLz2g8NQrsO0NdXtz9/zXIkVeclHg8Xj4h3/4h6AY643L5cJkUj3Gw4cP625fC9csXLiQ1NRUGhsbE7Z5/PjxsH3SNbRwTTwZQ9GKvNZHPdaQzeBIdAujZcXze9nhGJ0wgM8dVbim0lqA8A3Omf3SN6jgGICVNVBl8XGiE9JTvIA/KvuhaZT9Q/DJq6GxVW0d0dKtzqGNhBR5yUVBW1sbDz30EHv27EmKfZfLRV1dHfn5+UkJ2WjhGqPRSGVlZdwLohqKouB2uyMKcW1tLZOTk3R3R+EuzmB4eDgqkc/OzqagoCBmkR8a9YM3iuyUsjQEvqjaDvzoNwrPva6GUsanTKQYJjAY5pdIi8UCEy209IQPCWmZNStqoMqsxlbSUzykp6dHZV9bt/j8RwRPfl3wj7cIRsfVG1dLD9QsiPx+KfKSiwKtYlSPMEc4tNYA9fX1uou8z+djYGCA4mJ1+lCi3RsBJiYmUBQlokhqk6JOnjwZs/1oPXlQvflYr2d4VEEoo/Nm71SULyBNOKPy5P/jaYXfvaIw5VHw+Iykp0S37mGxWFDGWznRHn7/w81QWgiFuYJKixdQZ8dGEwqC05/PDRsFH9ki0Nr1NLZAS48iRV4igdMVo8lo1QuqyBcWFiZF5AcHB1EUJdhfJtFhG6CmT0LkPPaCggLy8vLiisvHIvJlZWUxPy2MjIFJzL/2UV5ejmGqg9Y5vOzT9hRae+Bk1+lq2sy06NoUWywWmGzlVKc37PZDzQor1fslVRbVZqpxMiaRD/19Z2UIqkvVm0dLD1RH7oYsRV5ycaB58mdK5GPN5oiEdoPSU+RHR9XRR/M1yIp38TVWke/q6orJ/uiEgRTD/P1irFYrntGT87YEaGxRv56aJvLRrUVoIt/eF36gR1MbLFXXl6k2qzeCFDERk8gPDAwEf2cAy6uhsUWhuRuqSyMPEpEiL7koOJPhGpfLFbNoRUI79zPtyUP8GTZJF/lJA2nGiXn3Ky8vxzN8gpbuyIJ9KHCJ3U7oCazNZ6VH58nn5eVh8nXjGErD55t9c2/pgdoyVYgLchRyMsEoxmISeWBaSGtFDTScUgeJVMtwjURyWig7OzuTkuKoefIrV64E0DXDxul0qqPmAsMxKioqcDqd0zy7WInGk4f4c+WTLfLjUymkmeavZFXHALbNK/KHWxQsgXKBhsASRE7kItYgQggKs9z4/Aa6ndO3acVKWkhFCFhoBaMympDIL68W7GsCnw/GBo5EfL8UeclFQX9/P4sWLQLgxIkTutvXpitlZWVRVFSU8MLoTNv5+fmYTGrHRS23PJFjnAlPPppiKFBFvqenB58v+lF9E54UMlLnF3l1DGArzhET4xErUuG69WAwECxWys2Kfp7qggJ10bW1F156W+HgSdWGVqwUGje/eZOgOPVY1CKflZVFYWHhdE++GrSI4G8f/V7E90uRl1wUOJ1O1q1bR0pKSlJCNponD/rMRw1Fy5HXCOfZxYrmyWdkRHZXa2tr6e3tjfmpIdoUSlBF3u/3xzQhatKbQkZq+IXOUHJycsg0qmsakdIoDzXD2kWCCrPadsCAh5zstKjPx2pOxSimeOhZhWv/SeHbj6kKrBUrhYZUvn6HoCp1W9QiD7MzkJYGMmzysqG/J7LTIkVeck4wPj7OXXfdFfQw9cbpdGKxWKitrdV98XV8fJyJiYmkifzM8Xza00IicXm3W622nC9PW0ujjKWHjdfrZWJiIiaRB2IK2Uz50shKj25htKJEFdy5ipUGRxS6+lXveGGZGus2ifGoCpU0SkstpAsH//MXKMw5vZDb0q0KcUHO9KeCsbGxmOzPzLDJyRRUWtQnhPk+NynyknOCxsZGHnzwQXbs2JEU+5pQLlq0SHeR16pokynyWo68RqKLr6Oj0cWEKysrMRgMMeXKR9tLXsNisSCEmFesXt6ncLzThN+v4FUyyEqPLoPJuiCfdJN7zgybwy3q15W1UFsGo+NgJPqYuXYNaZ4mPrgBfv55QVO7Go8PTXE8dOhQsIgt2s9fI1wtwcoaqJEiLzlf0B7Vk1WRqoU8Fi9erHu4JtkiPzNcA4mLvNvtnjceD5CSkkJ5eTktLS1R245V5E0mExaLZV6x+soDCs+8lo470MssNzM6kTebzWQIx5y58odOQXEemAsECwNZMMIfu8jndH2GZ78rqF8IUx41576lRwmK/Kc+9Snuu+8+QB+R/9Hdgn/668F5RzRKkZecE/T2qm5WskT+fPfk9Rb5WEQm1qHesYo8RJdhs9AKLb2mYB57blZ08mWxWDB6OyN48gorak4fA0D4o+tAGXoMR283QsCicjAZtYpU1ZOfmJjgnXfeCf6dxyvyofUXy6oFWYb5G7tJkZecE2ievN1uj7nt7HyMjY0xMTFBcXExixcvxuFwMDQ0NP8bo8TlcmE0GoPZJFarlb6+PiYn5y/WiYZkiXw0njzEftNKmsiXQZvDyHBgDTgvOzr5MpvN+Mda5lx4PdysxuPvueceTjVuV1/0RtdLPvQY4+PjuN1uUlMEi8oDFamBYqV33nkHj8cTLGyLVeQrKysZHR1lcHCQxsZGmprUcVDRVApLkZecE/T29lJXV4fH48Fut+tqO7SYKBlplC6Xi4KCAoRQH/XLy8uB2BYSIxEuJh/Os4sFbeE1GqIV+Q984AP87//+b9JEvrZM0NprDBniHblvjYbFYmFy6NicnvypLlhULnj44Yc5vO9PAPg9QzF78nD6iXR5NTScUmgPFCvt3bsXgL6+PiA+Tx6gpaWFD33oQ/zzP/8zoP6NzTUPQEOKvOScwOFwsGbNGmpqanQP2YS2BSgrK8NoNOqax65Vu2okOgQ7lPHxcfr7+zGbzdNer6ysZHJyMigasaK3J+92u9mxYwc7duxgZGQEk8lEenp61OcTbbjGNWKkU/11Upg79xDvUMxmM6MDjXQ4FLze6TdFr1cV4oLMIbq7uxnoa6YwF3xTgwmJ/Ioa2G5Xi5VqAiKflZWF0+nE7/fHLPJWqxUhBPfffz/Hjh3j6NGjgCryM4ewz0SKvOScoLe3F4vFwqZNm3jttdd0tR3qyRuNRhYsWBDzkIpIhObIgxqbT0tL00Xkd+/ejdfrZePGjdNe1wqi4g3ZxOrJd3R0RHxqOHz4MIqicOjQoWCOvPZkEw3RiPxj//VNAN49oYDiIz83ujx2i8UC4634/IIupzoMWxuI3dmvCvHkkLoY39PTw+IK8E04YhLhwsJCjEbjaU++SjCgPtBQaVbYu3cvW7duxev1BvvQxBIOSk1NxWKx8Mtf/hKLxcLJkyfxeDxS5CXnDw6HA7PZHBT5eAZVzIXT6SQ9PT34n6q8vDypIi+E0G3x9cUXX2Tt2rWzPPnS0lJMJlPcfeVj9eQnJiYYGBiYcx+t8+bBgwdjammgUVZWhsPhmDNTZGBggMf++zsIfBw4QWBgSHQiaTabYVL9nPY1wcrbFH7ypLpNG4Lt7FRDhD09Pfzqqwq+th/EJPIGgwGz2TwtXAOQnw0jgx10dXVx8803A+oULK/XG5N9OB2y+dnPfobX6+XkyZNS5CXnD5onv2HDBgYHB3XNgNEWLkNj5nqKvNbSIBTN+02UF198kWuuuWbW69rwkFhSG0OJxZPX1hgi3bQaGhrIy8vD5XJx7NixuEQeVJENx1/+8hf8vikyDb0cOK5EPZoPAiLvdZGe4uW27ygcaYU9B1VPXitWOn7UjslkUv8O89zg6Y/J0wb1iUET+cUVYDSejsenpqaydetWgGCbiFhFfunSpVx//fV8+MMfxmg0cvToUSnykvMDv99PX18fZrM5OJ1ezy6OM/PMY00JnI+Znjzok0bZ3d3NwYMHufrqq8Nur6mpiakSNZRYPHlNROYT+Q9/+MOAKmrxivxcv/fnn38eAJO3ndZeEfWQbYDMzEyys7MpynaTlgKfvuH0tKbmbjWPvaGhgQ0bNuDxeII351hFeNWqVfz85z/nkUceITUF6qxq+uTevXu55JJLKCoqIi0tLfg7i9X+gw8+yJNPPklqaiq1tbU0NTVJkZfox+TkJPfff39MTaSixeVy4fP51IKSnBwyMjLm9OjiYWYKol5etkayRP7FF18kMzOTyy+/POz2mpqauAdtx+LJp6enU1RUNOf1KIpCQ0MDmzZtorKykgMHDsQs8iUlJRiNRrq6uhgZGZkW//f5fGzbto3y8nKU8UBWVAwiD6qXfe2SnWz/qeCGywWnumF8UqtIVefyXnXVVUD8nvYvfvEL7rrrLu68806+973v8bEr4br1goaGBtatW4cQgpKSkrjtZ2ZmBp8uli5dSmNjI93d3VLkJfrw1ltv8fnPf56XXnpJd9vaI67ZbEYIQWlpqe4iH5qCqIVr9BrskUyR37JlC2lp4RcYtUHb8RCLJw+Rr6ejo4PBwUFWrVrFqlWr8Pl8MYu8wWBgwYIF3HHHHeTm5vLAAw8Et7399tv09/dzxx13MDkYaKsbo8ibzWYKxZusrhOsqAG/H461qzH5/PRBJiYmgk9MWguHWEU4IyOD73znO3ziE5/gzTff5N6/M/DpGwRtbW3U1KjVVsXFxXF78qEsXbqU3bt34/P5pMhL9EErVnr88ceTZltLQ9Nb5MOFayYmJoKVqokyl8h3dXUldCPZvn37nKEaUD35lpaWuI4RiycPkZ9+GhoaEEKwYsWKYD/9WEUe4Bvf+AZf+cpXqK+vD6YIghqqWbx4MZs3b2ZyKD6RD42XV1kgI+30+Dwx2UZqairr1q0jKysrKPKxxuQ1qqqqgllPfr+f9vb2YDZUIp58KEuXLg3eLOYTeVM8B7DZbAbgEWAhIIBPAcXADwA/cJfdbj9os9lKgf8BsoD/Z7fb9VcIyRlBy8d++umno+57Ei29vb2kp6cHbSbDk7fZbMGfQxcSZ1aSxsrY2Bijo6Oz7JSXlzM1NUV/fz8lJSUx2/V4PDgcjmDxVjhqamqYmJigp6eHBQvmGQ8E/OY3v2H58uVYrVZdPfmGhgYWLVpEZmZmQiL/d3/3dwA0NTVNO9aLL77Iddddp17jeKBRWhwirwmv0ShYWqnQcFKhow9Giw6zYsUKUlJSKC0tjduT16iqqgpmPTkcDqampoIiX1xczKuvvgrEfxMBWLJkSfD70tLIQ17j9eTXAGl2u30z8FXgi8C3gQ8CHwe+H9jvn1GF/wrgszabLfrqCMk5RV9fH9XV1fh8Pp555hldbTscjmAnQkiOyIeGazTPR4+4vFadu3r16mmvawVR8R5D698eSYi1EEA0cXlFUbjjjjvYtm0bU1NTeDyemEQs0mJ1Q0MD9fX1gLr4CEQ9MCQcM28ox48fZ9WqVaqYTQbCU97hmNsOaJ48qMVKf3lbzZF3tL0dPH9N5IUQ8/ban4uqqipcLhdutzt4Ywn15CcnJzEYDHOG4aJh6dKlwetKSYlcFBavyHcAwmazCaAAGAV8drt9wG63twHas+tlwA673e4F7MDKOI8nOcs4HA4WLlzIjTfeqHvIRkuf1EiGyId62qmpqZjNZl1Efs+ePSxcuHCWN7VgwQKEEHEfI5rJTSUlJWRmZkYVlx8eHmZycnLaAJB4PHntZhFasNbQ0BAU9yVLlmA0GuPy5EOPpX1ubrcbl8tFVVUVRUVFpBgmSTcMgd8dkwhbLJZpQ0mWVwsOBLJ0e1rfCD4xlZaW0tLSQmZmZkzFXKGEFqq1tbWRkpIS/PvWnI2srKy47YNa2FdcXDxvqAbiDNcA/YAHOAqkA5uB+0K2e202WyqQYrfbtaqWIU6LfxCbzXYncCfA5z73ueAKdzLweDy6ps6dac7m+be1tZGdnc0111zDnXfeSWNjI3l5eTHZmOv8m5ubyc3NDW5LS0ujq6tLl2udmppieHgYRVGm2bNYLDQ2NsZ8jJnXsH37dtatWxfWjsVi4d1332XdunUxn7cWMhgdHY14jhUVFbz77ru8733vi2hP8/Z7enqCfXvmsx1KWloa/f39/OpXv+LRRx+loKCA6upq/H4/J0+epKSkJGjrhhtuoKqqKu7fn5Zd1draGvwctL+JkpIS/BNPMjD+WkxptiaTCYfDQUdHB0IILDlpQCF5WX66DhwhO/vjdHZ2kp2dzdTUFDk5OXGfv9Go9tTZt28fJ06cYMGCBcFGYtqc3oyMjIT/vmtra8nOzqazszP45BiOeEX+asBrt9uX2NRg54+B0Oczk91un7LZbB6bzWYICH0eMGuly263PwQ8FPhRn3SHOZjvwzjXOZvn73a7WblyJVdccUWwGjXWc5nr/N1uN1VVVcFty5Ytw+VyUVpaGvwPEy/a4/KyZcumHbumpoaRkZGErsHv9/POO+/wox/9KKyd2tpahoaG4vqdaaKwcOHCiN7a4sWLcTqd8x5DE/n+/v6gl11bWxv1uWnhqO9+97uAuthstVrp7u5mcnKSdevWBW09/fTTUdmci/r6evx+PyaTKdjJ02azkZ6erj6BnbyHPIMhps912bJlTE1NkZ2dTX5+Pu9VFEChqlShYWSE+vp6rFYrCxcuBNQ1hUT+r1ksFsbGxhgaGqKmpiZoS3viS9Q+wN13343RaJzXTrzhGgFoc8n7gRzAZLPZ8m02WwWnxfxtYIvNZjMBlwD6jbCXnFEcDgclJSXBsIfWD0Yv26Fl+6WlpcECqUR5++23MZlMwQVBDT2qXg8fPszQ0BCbNm0Ku72qqiqhilSYP6QSba68FqpwOBxB27Fm1wAcO3aM97znPcEQkXZ92vqAHoQ2eGttbaW0tDTY7MxisdDf3x/zoqj296XF5WsWQFoqmHPUlpbaYrwmwolkvoAasmltbZ2WWQMEF+ETtQ9w22238clPfnLe/eIV+ZeACpvNtgv4LfBN4N+B5wM/fzWw3/cD378KPGi32/VtFC45Y/T19VFSUkJeXh4Gg0FXkQ8Xk4e5S9xj4Y033mDNmjWz4rd6FETt2bOHoqKiaZkOoVRXV8fdWyZaIY626lUTt3hj8lrTtU2bNvE3f/M3QXFvbm4mIyMjrgyiubBYLBiNRjo7O2lra5smkppYxyqS2t9Xd3c3H/zgB3n44f9iZQ3kpat/xzM97URFWMuwmXn+oTH5M0Vc4ZrAQupHw2y6fMZ+3UDyguySM4Lf7w+2uzUYDBQWFibVk9f+Q+oh8nv37mXDhg2zXtfDk9+zZw8bN26ccwEtUU8+IyNj3nBVTU0NHR0deDyeiFkWmiff39/P8PAwEJvQCCG4++67ueWWW+jv7w8es6Wlherq6oQWEWdiNBopLS0NevJaqwuIX+Tz8/NJSUnha1/7Grt37yY3N5enfvxp/vTMa+wsKgpm6mgin0h6I6i/+zfffHOWyOfl5ZGSknJGRV4WQ0nmZWBgAJ/PF/TWioqKdBN5t9vN2NjYNE8+LS2N/Pz8hEV+amoKu90+p8gPDw8HB1zEw549e+YM1YDqyff19TE2Nhaz7WhrEWpqavD7/fO2HO7t7SUvLw+fz0dLSwvp6ekxr3f85Cc/4fLLLw8uuHZ0dARFXm+0m/BcIh+rCAshMJvN7N69m8rKSpqamqgtEww4TgZDNaBvuKapqQmHwzFN5LXzSPQmEgtS5C8gktFXBk4XQiVD5LUFxnCtdBMV+QMHDjA5OTmnyEP8gz1GRkZoa2uLmDmjiVM8IZtYRB7mz5V3OBzBxdMTJ04kJGLadTU3N9PS0qJrPF5DS9mc6QlrzkA8519WVsaGDRv45je/ybFjx1AUhY6OjmALX4j/SWEmVVVVwf83oecP6jVIT14SM/v376eoqCgYy9UT7Y9V+w+gp8jv37+f1NTUWZWdeoj83r17KS0tneYJaiRarKSFPPLz8+fcJxGRj7YiNScnh8LCwnmP0dvbGxT548ePJ1SxnJeXR0FBAS0tLTQ3NyfFk7darbS2ttLZ2alLuAbgV7/6FX/84x9ZtmwZo6OjdHV10d7ePs2TT0tLo7CwUBeR1wi9iYCa1RT65Jps4k2hlJxjHD9+nKGhIQ4dOsT69et1te1wODCZTEFB01Pk33rrLdauXTur+k8vkd+wYUPYeHFWVhb5+flJLVbKyMjAbDYn1ZOH6Fona2GDvLy8hD15OJ3V09ramjSRf/jhh/H7/bosvIKaRgkE1y6OHTtGR0cHV1xxxbT9LBZLwuEU7ZwLCgpmFYY9/PDDmExnTnqlJ3+BoM0x1Sb06ElfXx/FxcVBsdRb5C+77LJZr+sp8nORyOJrtMOqq6ur41p8jVXk57uO3t5ezGYzFouF5ubmhHsPVVdXs3fvXjweT9LCNdpaRqhXrP0dJnKTys/Px2w209TUNMuTB7jmmmum9TqKh4KCArKzs2eFakBt+SBj8pKYSbbIh8bM9RJ5r9fLvn375hT50F4jsdLf309bW1tY2xqJDA+JNo89tFlVrPb18uQnJiYYHh7GYrFgNpvjGj03k+rqal5//fXg93qjhdNycnKmhcRMJhMlJSUJn/+SJUuw2+2MjIzMEvmf/vSn3H777QnZF0JQVVUVVuTPNFLkLxCSKfJaIZSGXiJ/+PBhxsbGeM973jNrW6KevPZ5ROrOqIcnP5/YnAuevJY+aTabgzfrRD15rQNmVlZWwp08w6EJb2Vl5axw26pVqxJ+eli8eDEvv/wyMDtmrhdr166d1bjubCBF/gyxf/9+Nm3apNugipn09/djMBhoaGjQ/RhaIZSGJvKJHufNN98kPz+furq6WdtKS0sZHFSHOcTD0NAQQMT+OokURLndbjIzM+dNQzwTnvx81xHar1+v7BHNe6+pqdE1R15D8+TDLZq/9NJL3HnnnQnZX7JkSfDmm6xWIY899hjf+ta3kmI7FqTInyHeffddXnvtNV27K4bS39+PzWZjaGiI9vZ2XW2HC9d4vd6EcszhdDw+nEgkWvUajcgn6slH02mxqqqKrq6uYA+WaInVkx8YGAhWss5EC3uVlJQEszr0iMmHftWbzMxM8vPzw4Y79LipLF68GFD/ls9kfPxsIEX+DKFNIdK66ulNf38/733ve4PevJ7MDNdoU5ASDdnMtegKp8MsWh59rAwNDZGamhrseRKO8vJy+vv743pacLvdUYm8JoKx3nhjFXmYO+e/t7eXwsJCUlJSdOudkmyRB7VR2Zo1a5JiW2tFMTMefyEiRf4MoQliMkW+srKSRYsW6S7y4cI1kJjIu91uDh8+HDYeD2oWRUpKSkztZEMZGhqatxWy9h88nmOMjIxEJcJauCHWuLyeIq8NZQF0i8lnZ2djsViCXRuTwc6dOxMOy8xFbW0tRqPxohB5mSd/hki2J69NP1q1apWuIq/1rdFb5Jubm/H7/cHc5Zlog53jzX6JReQ7Ojqora2NyX604Zpoi5VmEovI5+bmkpOTM2foSUufhMQqRmfywgsvJFXkkxHr10hNTaWmpiZpi67nElLkzxDJ9OTHxsYYGxujuLiY+vp6fvOb3+hmW+tbExqTT09PJzMzM2FPHiKPiSsrK0uqJ5+Xl0dmZmZccflYRDieRmWxztGNtPiaDE8eSFoo5Uzxta99Leyi/4WGDNecIZLpyWtiq3nyTU1NMS/0zcXMvjUaiaZRRlNMFGl49HwMDg5GbDkAqqcY7+JrtJ48xN5y2O/3xzxoO9J1hHry2dnZ5OTkxDzV60Lk1ltvjVgsd6EgRf4MoU3S0Uav6YmWE15cXExVVRV+v3/aPMtEOHr0aNjYZaIi73a7MRqNEYcZJ9uTh/gLomL15GMR+fHxcRRF0U3kQz15IQQ7duzglltuidq25PxGivwZwul0ctlll+F0OoPpfXqhiXxRUVHQY9NL5Hft2oXNZpsVw9XDk8/JyYkYd03Ek49F5M+EJx9NuMZut+PxeKKupg0l0s0q1JMHdZTehZ42KDmNFPkzhMvl4tJLLwX0D9n09/eTmZlJZmZmMKyip8jPbOAE+ol8JMrKypIu8vEWRMXqyXd2duL1eufcx+Fw8J73vIc//elPcYu8dh3aDF6AyclJHA7HeT3bWJIYUuTPAFNTU7jdblavXk1KSkpSRF4bK5aamkp+fr4uIj84OMiBAweSIvLRiKTVasXtdsdVdHWuefI+ny/icbZt24bf76e1tTXuGay9vb20tbVRXl7Otm3bgNNpledCDxXJ2UGK/BlAW3QtKSmhpqYmqSIPagaFHiK/Z88ehBBs3Lhx1rYz4cmHDnSOlVhEvqenJ6KXHY5oi6Egur7yzz//PKCmc8bryQP81V/9Fd3d3ezfvx8gODFKivzFixT5AK+//jo33HBDUmxrIl9YWEhdXV1SRD60SZTZbE6og6PGrl27WLNmTVixPBOefFlZGRBfsVIsIu/3+2NunxBtMRSorW1zcnLmjMt7PB7+8pe/kJqamrDI2+32acO929rayM3Nldk0FzEyTz6A3W7nueeem+UV64EmhoWFhSxcuJCDBw/qan/mOVssFl08+bni8XBmPPmcnBxycnJi9uQVRYlJ5IGwfcUj2Y8lXCOEiJhG+frrrzM0NMRHP/pROjo6GB0dxWQykZqaGpV9UH8fubm53HnnnYyPj3P06FGAWePzJBcf0pMPMDAwAKC7AIPqyRsMBvLy8li4cKHunrxW7aqhR7hmZGSEd955J6LIj4yMMDU1FZf9aBcu40mjHBsbw+v1RiXyJSUlpKWlzTsIO5SJZwPoMAAAIABJREFUiQn8fn9Mnnakgqjnn3+eVatWsWHDhqAnn52dHVPFpxCCAwcO8L3vfS84tQnUm5cU+YsbKfIBNJFPRj92l8tFQUEBBoOB6upqOjo6Yo4BRyIZMfnDhw/j8/mCGUEz0Y4XrzcfrSccTxqllqI6XzEUqOJYWVkZlcg/9dRTTE5ORj0VKpRInvyf//xnrrvuumAa5PDwcFwVqTU1NRiNRmpqamhra8Pr9UpPXiJFXkOLmydD5J1OZzBmbjabURQleDw9SIbIa0JWUFAQdnto35d47UcjkvF48tG0GQ4lmmKl5uZmPvrRj7Jnz564YuZzefJjY2McPnyYK664gvLycrxeL6dOnUqo7UBNTU0wm0eKvESKfIBkh2u09rxaHrtWwJQoiqLMKfKJDPVwu90YDIY5W/WWlpaSmpoa19QjzX40QpaIJx+tyFdWVs4r8na7HVBbH8frybe3t0/LYYfpKY7ajfPo0aMJibzWbK25uZm2traLogmXZG7OG5Hv7e0NjutKBgMDA1itVg4dOoTP59PVttPpDIq8JsZaT5hEGR0dZXJycpbIezyehCpr54sLGwwGqqqqglkcsRKLJ59ska+qqpo3XKOJfE9PT9CTj0Xkq6qq8Hg8s/rja9dmtVopLS3FaDTS1NSUkMjn5eVRUFDAO++8g9vtlp78Rc55I/KPP/44f/3Xf5208Xkul4srrriC8fFx3RdGXS5XMFyTl5dHSkqKbiIf2rdGQ4/WBtE0yKqpqTkjnnx3d/csDzgSQ0NDpKWlReyLE0o04ZpQkdc8+ViEWBuu8eyzz05zIjo7O8nMzCQvLw+j0UhpaSnNzc26zGDdtWsXIHPkL3bOG5F3Op24XC7dwhwzGRgYYMOGDaSkpOgelw8N1wghKC4u1u06wom81owqkVz5aEQ43iHVEJsn7/V6Y7opRps+qVFZWcnQ0NCcTz5+v599+/aRlZUV9ORTU1NjSnEsLi7m9ttv57Of/SxLliwJTorq7OzEarUGn5jKy8tjbk4WjpqaGnbv3o0QQrY0uMg5b0Rei5lr+b96oi2Ems1mli9frrvIh4ZrQP0Pr5cnf+jQIdLT06eJfH5+PiaTKSFP3u12z1tWX11dHVe4xuv1Mj4+HnV2DcRW9RqryGsVqXOFbE6ePMnQ0BBbt24NevKxirAQgl/+8pecPHmS/v5+XnzxReC0yGtocXk9RH5wcJCysjJSUlISsiU5vzlvRH5wcBBIjsiPj48zNTVFQUEB9fX1SfHkQytSS0pKdPPkf/3rX3PTTTdN8yqFEAln2MTiyccaQtMGTkebJ28wGKKakaqlcw4ODsYk8uXl5Qgh5gzZ2O120tPTufrqq+nu7o6ppcFMampqWLJkSbDldGdn57QiLD1FHmSoRnIeiXwyPXnNdmFhYdJEPhmefFdXFy+//DKf/OQnZ207EyJfU1PDxMREzMeJJTvFZDKxYMGCeUW+tbWV0tJS9u/fz9DQUFQ58hqpqaksWLAgosivWbOGqqoqhoaGcDgcCYlwXV1dUOQ7OjqS4slrGTZS5CXnncg3NTXpblvLWS8oKKCmpob29nbdFngnJycZHR2d5cnrIfK//e1vKSgo4Jprrpm1LVGRj2bhVVtMjCZk4/f7ee6551AUJeY884qKinlFfseOHXi9Xnbt2hVzuAYiZ9js27cPm80WFOOmpqa4PXmYLvLJDNeAFHnJeSbyZrM5qZ58QUEBJSUleL3eYHgoUUKbk2noFa55/PHH+djHPhY25nomPHmLxUJ6enpUi6+vv/46n/nMZ2hsbIw5zzyailQtk2Tv3r1xifxcufLaomuoyB89ejQhkV+0aBHHjx/H5/PR3d09TeS17xMVeW2dQYq85LwS+fXr19Pc3MzExITutoUQ5OXlBdMP9VoYDW1OpqFHuObo0aPs378/bKgG9BH5+RZetcZb0Yj8gQMHADXEFKvIR+PJ79q1i7KysrhFfq40yvb2dtxuN6tWrSI/P5+MjAyOHz+ecLhmdHSUhoYGfD5fUjz59PR07rnnHrZu3ZqQHcn5z3kh8oqiBEXe7/frPifV5XKRn5+PwWDQfbKS5smHC9ckEhI6fvw4wJy9ZRLtRBltHnu0Iv/uu+8Cqshr4ZpoR9DNJ/JtbW20tLTwxS9+kfb2do4cORJ3uMbv9/PHP/4x2FtI+1urq6tDCEFpaSkTExMJh2vg9NNHqMhrC825ublx29f45je/GTyW5OIlrlbDNpttA/DdwI9lwJ+BPwA/APzAXXa7/aDNZisF/gfIAv6f3W5/PJ7jjY6O4vP5uOSSS0hJSeHo0aOsXLkyHlNhGRgYCPZoKSgowGg06ubJ9/T0YDKZpolCcXFxcFpUvGKhNbEyGo1ht5+JcA2oIq91PIxEqCdvMpnIzs7GYIjOx6ioqKCzsxOfzxf2enft2kVmZiZ33nknX/3qV3E4HHGFa7q7u7nrrrt46KGHeO655/jgBz/IyZMnKS4uDoquHsVKRUVF5OXlsWvXLgwGA6WlpcFtaWlpvPTSS1x++eVx25dIQonLk7fb7XvtdvsWu92+BXgdeAb4NvBB4OPA9wO7/jOq8F8BfNZms4VvhDIPWsy8pKSEuro63RdfQ0XeYDDomse+c+dO1q9fP03QtKeFRI4xPDwc0dszm824XC48Hk9c9qMV+WiqXr1eLwcPHkQIEfTkYxHJiooKfD7fnIM9Xn31VTZs2EBOTg5r164Fom9poFFVVYWiKDzyyCPk5ubS2NgIqDnyCxcuDO6nCXIinrwQgrq6Ol599VVKS0sxmab7WldeeeWcPYMkklhJKFxjs9lSgcsAO+Cz2+0Ddru9DdAC0JcBO+x2uzewT1zud+jC6NKlS3VffJ2Z4lhSUqJbuObFF1/k6quvnvaaVriUyOLryMhIRJHXql7jvY5osmvgdAvdSG0HmpqamJycZPXq1cGYfCwiqTXYmitkEzrcZP369UDsIl9bW8uKFSt4/PHH2bx5M0eOHOH/b+/sw6K8z3z/GWQE1CIBAZE3BQURjYK/YohpNIk2b3+kydlNdnO1p7Y9Sa9z1m2uK3tO0m023Z5serVJr+yVbfct3WY33d0/kt3tldMktU1SMVGjQX4R3wiogyKgqBiVFwPIwJw/nnmGAQbmeeYFZsb788/gPDO/uRl5vnPP97l/9w1Ti3y4nvmKFSu4fPmy7EYVok64k6G2ADuBhUCv3/1u7weAU2ttnv09jIm/D6XU48DjANu3b2fr1q2TXuTEiROAsWkpPz+f3bt3hzT3c3h4OODzzp49S2pqqu9Yeno6p0+fDuk1/Glra6O1tZV169aNW8vMrltaWmyd5P7xT4x5Iqbfb3rhdjEHggR7D+bNm8fQ0BCNjY3jbAd/6urqSE1NpaamBq01xcXFpKSkWH5/R0dHcTqdHD58eFJHxUuXLnHy5EkqKio4e/Ys5eXlwNT/19Px7rvvArBr1y4aGho4e/YsLS0tbNmyxbeW+S1sZGQkrL8P80M4MzMz7L8zO4TyvsQSEn9gptORcEX+D4F/Aa4C/mllstb6ulJqWCmV5BX6hcCkJupa658DP/f+M+CVyOTkZJKSkigvL2fNmjW88cYbIWVAE2uSTYaGhiguLvYdKyws9H2ghMPbb7/NTTfdxH333TfJS87IyGB0dNTWa/jH7/F4yMrKmvL5OTk5OBwO268BhoANDg5SWFgY9Llm+ebQ0NCUj+3o6GDt2rUUFBTwu9/9DofDQWZmpq24CgoK6O/vn/Qcs7Ty9ttvJycnhwceeIAnn3ySqqqqkP//ampqeP3111myZAnt7e1UV1f71jJvCwoKwvr7MG2l5cuXz2g2P9U5EC9I/PYJ2a5RSjmBLwJ7tdafA8lKqQylVCFjYt4AbFZKJQPrgaZQXuvKlSssXLjQV/3y2Wef2epKGIxo2TXvvvsuW7ZsCXixMNwmZcE8eafTyaJFi2wPqAZ7bQeys7OZN2/etL78oUOHWLt2Lbm5uXR1ddHb22vb0y4qKgpo17S1tZGWlubLsPPz87l06RJlZWW21venoqKCnp4ejh49Sl9f3zi7Ji8vDwjPk4exChurc2UFIVTC8eS3YPjtptr+BbADeB34c+99L3h/3g38o9Z6IJQXunr1qu/CaHZ2NqOjoxGdrOR/4RWMLDjcC6/Dw8PU1dVN8uNNwt31GsyTB0OQJvYvt4KdHalmrfxUu149Hg+HDh1i3bp1LF68mOHhYdra2mx72lOVUZ45c4bi4uJxfe/D9csrKioA45sYEPELrzAm8vGclQrxQch2jdb6t8Bv/f69G7h1wmO6gMkmu038Rdi/MsW/82Kk1jdfI1yRb2hooLe3N+A1Bgh/Q1Rvb29QgVi8eHHUM3mYvla+q6uL7u5u1q1b5xPi48ePB2zFMB2FhYX8/ve/x+PxcOTIEW6++WYcDgdtbW2+3Z2RIiMjg8WLF/POO+8wf/58n38Oxnu6fft2lFJhvUZOTg5PPPEEd9xxR7jhCsK0xMVmqEAiH8nxeVeuXJlk14S7WcnlcrFw4cIpBSjc1gZWLI/FixdHPZOH6csozRr6srIy327ijo6OkDP5V155hXXr1vHJJ58AY5l8pFm1ahX19fWUlJSM+5aQlJTEz372M9/fYag4HA5efvllsWuEqBN3Ip+RkRHRzUp9fX2MjIxMsmvC7V8z8dvBRML9thDMkwfDrgklk7cr8tNl8p2dnaSmppKZmUlKSopv569du6OwsJDz58/zxBNPAGNVQ21tbb5GaZGkoqICj8czzqoRhHgkbkTebB0b6c1K/jX4JpHYrBRM5MO98GrFkw/VrjFFPljvGhOzVj7QbNzOzk5fv3Ywtu1DaCIPcO+997Jx40aOHTuGx+OJWiZv+vIi8kK8EzciH2nP3GSqLpEQXv+ay5cvRz2Tt2rX2LWdzPF2VicKLV26NOCQahgTeRNT5O3aNZWVlTz//PP88pe/ZM2aNRw7dozu7m4GBgailsmDiLwQ/8SFyPtX10BkRT5QJh+J/jXBMvnc3Fx6enoYGLBfcDQ6Omq5umZwcJDe3t5pHwfGzs4VK1bQ399vu+2A2bs8kGUzcfJRqJl8cnIyzzzzDAsXLmT16tUcO3bM1zUyGpl8ZWUlDoeDlStXRnxtQZhJ4kLko5nJX7lyhTlz5owTtUhYQsFE3uzzHaxPeiBMO8WKXQNYuvh6+PBhXC4Xp0+fttzSwCQzM5MFCxYELKOcOPko1EzenzVr1nD+/Hk++eQTnE6nr3Y9kuTm5nLo0CE2b94c8bUFYSa54UW+o6OD3NzccRUU5muEY9dEU+TNfuxWRd6KL9/Z2em7tZvJT9dXfiq7Jpw688rKSgDeeecdioqKLHeztItZpikI8UzMi/zAwABDQ0NRE/l9+/b5mlr5E+6GqGAiv2DBAjIzM6ecKzodpv0STCjT09NJS0uzJPLmRiNzSIbVi64mgcoo3W43XV1dERf5rKws8vLy2LlzZ1T8eEFIJGJe5E3P3H8wc6RE3uPx8NFHH7Fx48ZJx8J9jWAiD9PPFZ0OU+SDZfLmkAsrdk04mTzg2/Xa1dXFU089xeDgIBcuXGBkZCQiF14nsnr1agYHB6PixwtCIhE3Ih8okw932HZbWxvnzp3jtttum3Qs2nYNTD1yLhhWRR6sl1FOzORDEXmXy8XDDz/MT37yE/bv3+/rtufvya9bt44f/ehHYfWWAcOXh+hcdBWERCLmRd7ckDRR5IeHhy1VjUzH3r17SUtL83UE9CccuyaQxRSIqYZHB6Ovr4/k5GRLgyWs9q/p7OwkOTmZzs5O2xdewbBrOjo6OHz4MDk5ORw4cMC3prnTFWDu3Ll897vfnTQowy7mZDCxawRhemJe5KeyayD8Ydt79+7llltuCVgPHk4mb8bsX3sfiHDsmi984QuWLgpayeRHR0c5e/YsVVVVIWfyZqnha6+9xp133ukT+SVLlkw5ojAcTJGXTF4QpicuRD49PX2cUERK5Kfy48HIgLu7u30Dne0QyGIKRHFxMR0dHQF3ik6ktbWVb33rW4yMjFhqaWBipbXBhQsXcLvd1NbW0tnZSV9fn22Rr6io4OLFizz00ENs2LCB+vr6SZU1kaSqqoof/vCHbNiwISrrC0KiEPMif/HixUlimZWVhcPhCEvkL1++TFNTU0A/HgwfeWRkhAsXLthe26rIFxUV4Xa7LXnmO3bs4N133/WNz7Mq8lYuvJoXXWtra7l27RqdnZ22q2tg7MO3pqaGs2fPcuDAgai10k1OTuZ73/uezEIVhCDEvMjX1dVRW1s77r45c+aQmZkZlsjv37+fpKSkSWubmBmoKYB2MEU+2JxR02qw4ss3NjYCRldHO5n84sWLuXTp0rQDvTs6OkhOTmb9+vUAnD59Oqzql6qqKubMmcOePXuky6IgzDIxLfIDAwPU1dVx//33TzoWbonjmTNnyMvLm1Isc3JyfBci7WJOsgrmRWdnZ5OammpJ5A8ePAgYAmxnspIpstPNlTS9c3OD1ujoaFgin5aWxs0338zo6KiIvCDMMjEt8rt27WJoaCjggIlot+pNSkoiPz8/ZJEPZtWAUcdeVFQU9OLr4OAgTU3G5ERT5K1m8la+LXR0dFBYWEhKSoqvEibcOnbTKxeRF4TZJaZF/je/+Q0bNmwIOKAhEkM3ggllfn5+SJPVrYo8WKuVP3bsGG63mzVr1nD69GlbnnxGRgbp6enTzmD1v0Bq3oYr8jU1NYCMtxOE2SZmRd7j8bBjx46AVg3MzNCNgoKCkDL5YG2G/bFSRtnY2EhmZia1tbW2PXmHwxH0g8TM5GGsb3soF1792bRpE5mZmaxYsSKsdQRBCI+YFfnm5mba2tq47777Ah6PxCDsYL52qCJvJ5O3siHq4MGDVFVVUVhYaNuTh+knN0F0MvmSkhI+++yzcRuhBEGYeWJW5Hft2kV2dnbA3ahgXBgNpbzRJJqZfCh2zXQtGg4ePEh1dTVFRUWcO3eO7u5uy5m8/2sEYmRkZFzPdzOTD1fkBUGIDWJW5Lu7u8eNjZtIfn4+586ds7SRKBB2PHm7PXLsiPyyZcvo6+vzlV1OxO12c+TIEZ/Ig5F52xH56TL5ixcv4na7feIeqUxeEITYIGZFvqenZ9o688LCQkZGRkKaYQrWZqQWFBRw/fp12xd47Yh8SUkJYNS/B6KlpYXBwUGqqqrGVarYsWvMnbWjo6OTjpmNycy1zSlPwWr8BUGID+Ja5GFMpOxixdcOdUOUHZHPy8sjJSVlSpFvbm5m7ty5LF++nLS0NN8gELuZ/FQzWFtbW8etW1tbS319fcCKJkEQ4o+4FfmcnBycTmdYIm9lRqrD4YiqyCclJbFs2TJaW1sDHm9vb6ewsNC3scrM/O168hB4BqvL5aK0tNQ3XcnhcPjKHwVBiH/iVuSTkpIoLCyMqsg7nU5yc3Mtify+ffuoqanhypUrltoM+1NSUjJlJu9f3ghjdoodkV+0aBHz5s0LePHV5XKxfPlyy2sJghBfxKzIX716NagvHKrIj4yMcO3aNUtCabXCZvfu3TQ0NPDyyy8DwdsM+zOdyLe3t/suuMKYyNvx5M1a+akyeRF5QUhcYlbkg2XyYIh8KP3Y+/v7AWtCWVBQYGnXa0tLCwAvvfQSELwDpT+lpaW2Rd5OJg+GLz9VJi8blgQhcYlpkfcfFBKIUDN5O+PzrGbyLS0tPPLIIwwODgL2RL6kpIT29vaAnSInivytt95KbW2t7eoXM5M3u3r29/fT29vLxYsXJZMXhAQmpkU+WnZNX18fYE3krTQp83g8tLS0cPfdd/Poo48C9koQS0pKGB0dnfStZGBggO7u7nEiv3LlSvbt22d72lJxcTFHjx7lkUce4eOPP2bv3r24XC4AEXlBSGDCG7QZJQYHB7l+/bolkb9w4QLXr19n7ty5lte3k8mbHyQej2fKjVkXLlygp6eH8vJy7rjjDsrLy22JsGnBtLa2Ulpa6rvf/ADzF/lQWbp0KV1dXaxdu5bs7Gw+/PBDqqqqSElJkU6RgpDAxGQm39PTAwTPhgsLC/F4PLY7RZoib8WTLykp4fPPP5+2hcLx48cBKC8vZ+nSpTzzzDO24pk/fz65ubmTfHkzs/evrgmV9evXs3r1an71q1+xZcsWdu/ejcvloqSkxFc+KQhC4hGTZ7dVkTczXLuWTW9vL06nk5SUlKCPNTNr09oIREtLC9nZ2WRlZdmKwx+zwqa9vZ0XX3wRj8dDe3s7mZmZEWkxsGLFCo4ePUppaSmbNm2ioaGBI0eOyEVXQUhw4lrkMzIymD9/vu0KG7OlwVT2iz/Z2dksWLBgys1KYIj8ypUrbcUwkdLSUlwuF1/72td4+umn+fTTTydddI0Ut99+O8PDw7z11lvixwtCghPXIu9wOEK6+Gq3H/vy5cujLvIlJSW89dZbfPzxx2RkZPDee+9FTeSzs7OpqKhgYGBARF4QEpyQL7wqpTYDz2J8UPwU6AZeBEaB/6m1PqqUWgz8KzAf+Aet9b9bWfvq1askJyeTlpYW9LGhirydzUSlpaVBRX7r1q22YphISUkJIyMjPPfcc7S0tPDee+/hdrspLy8Pa92p2LRpE83NzSLygpDghJTJK6XSgD8D7tVa36G1fhP4IXA/8CjwgvehT2MI/ybgT5RSqVbWN2vkrdgpoYi8nfF5ML3IDwwMcObMmbDFeOvWrTz99NM89dRTfPnLX+bDDz/kxIkTUcnkwRB5kPJJQUh0QrVraoEB4G2l1JtKqTxgRGt9RWvdDph7+muAOq21G9DAaiuLW6mRN7Eq8u+//z7btm0D7Nk1ML3Inzx5Eo/HE7Zds2TJEn784x+TnJzM1q1bGRgYiJpdA/DAAw/wi1/8gqVLl0ZlfUEQYoNQ7ZpcYDlwC7AF+L9Ar99xt1JqLuDUWptNzHsYE38fSqnHgccBtm/fztatW+no6GDevHmWSiMzMjI4deoUnZ2d02b+r776Kjt37uTUqVOcP38ep9NpufRy4cKFXLp0iZaWlkk2z759+5g7d66t9axQWVlJU1MTaWlpvnWHh4cj+hr33HMP586di9h6wYh0/LNBvP8OEv/sEq348/PzpzwWqshfBT7SWl9XSu3EEPk+/3W9x4aVUkleoV8IXJ64kNb658DPvf/0gLGDdNGiRdMGbrJhwwb6+vpITk729USfiMfjYf/+/Xg8Hq5fv47b7WbJkiWW1jdfAwxrZmLGfvnyZUpLSyOecd9///00NTWxfv16X5xnz561HHMsEu/xQ/z/DhL/7DIb8Ydq1zQAFUopB7AO+BRIVkplKKUKGRPzBmCzUioZWA80WVncjl1TVlYGwIkTJ6Z8zOnTp31llq2trbY9+cLCQpxOZ0DLJlpdHB9++GGqqqrIy8uL+NqCINw4hCTyWutLwJvAhxgXVp8D/gLYAbwO/Ln3oS94f94N/KPWemC6dY8cOQLYE/msrCyysrJ8u04DsXPnTtLT08nNzcXlctn25OfMmcOyZcsCbog6efJkVER+/fr1HDx40HaPGkEQBH9CLqHUWv8d8Hd+d7UCt054TBdgubbwK1/5CqdOnaKnp8fWVv6ysrJpM/m6ujo2b95Md3c3ra2ttkUexi6+Xrt2jba2NiorKwEjkzebkgmCIMQaMbUZqq2tjWvXrlkaGOJPeXn5lJm8x+Ohrq6OO++8k+LiYp9dY6dOHgyRb2hoYOPGjSilGBwcpL+/n/Pnz0sZoiAIMUtMibzZstdKL3l/psvkm5qauHjxInfddRdLly4NK5M/fPgwg4ODDA4OcvjwYZ9HLyIvCEKsElOthhcsWEBTU5MtTx6MTL61tZXh4WGcTue4Y3v27CErK4vKykpfJu92u22L/IMPPkhXVxfPPvssK1as4MCBA+Tl5eF0OiPSJVIQBCEaxFQmX1FREZLIl5WV4Xa7fTNMPR6P71hTUxNr1qzxzTl1u92A/fF5xcXFvPDCCyxYsICamhrq6+txuVwsW7aM5OSY+qwUBEHwEVMiX1lZySeffMLw8LAtkV++fDkOh4Pjx4/zyiuvsHbtWt+x5uZmVq1aBRhCbWLXk/enpqaGAwcOyHxUQRBinpgT+fr6esDe+LzU1FSKi4tpbGzk+9//PkePHqW7uxswRL6iosK3ZmamsenWbibvz4YNGzh58iQHDhwQP14QhJgmpkR+1apV9Pf3A/ZEHgxf/sUXX2RoaAiAxsZGrl69SldXl0/kYewiaTgir5QC4OjRoyLygiDENDEl8mbtOdgX+bKyMvr7+3nqqacoLS3l4MGDNDc3A4wTeXPSUzh2TUZGhq/rpIi8IAixTEyJfFFRkW/UnV2Rr66uZsmSJXznO9+hurqaxsZGmpubSU9PH9caoLS0lHnz5oW9k7SmpgYQkRcEIbaJKZF3OBxUVFQwd+5cUlMttZ738fWvfx2Xy8WCBQuorq72ZfKrVq0a153ytttu8zUcC4dbbrkFp9M57mKuIAhCrBFztX+VlZW0tbVZGhjij8Ph8E2SqqqqwuVyUV9fP86qAbj77ru5++67w45z27ZtrFq1alJdviAIQiwRU5k8GBlyuNlxVVUVYGyEmijykWLevHls3rw5KmsLgiBEipgT+ccee4w9e/aEtUZOTg4FBQUAURN5QRCEeCDmRD4pKcm2Hx+I6upqQEReEIQbm5gT+UhRXV1NamqqzDAVBOGGJuYuvEaKb3zjG5SWlsrQDUEQbmgSNpMvKiriq1/96myHIQiCMKskrMgLgiAIIvKCIAgJjYi8IAhCAiMiLwiCkMCIyAuCICQwIvKCIAgJjIi8IAhCAiMiLwiCkMA4PB7PbMcgCIIgRAnJ5AVBEBIYEXlBEIQERkReEAQhgRGRFwRBSGBE5AVBEBIYEXlBEIQERkReEAQhgUmYyVBKqYXA+8Aq4Bat9TGl1J8BDwH9wDatdZc17S3nAAAFL0lEQVRSKgX4KbAC6NNaP6CUWgv8AzAM9AJ/pLW+Fkfxbwb+DWgFRrTWd81k7BGI/0HgCe9SJcBLWuu/iaP404F/B74AaK31/5nJ2G3G/wpQ7n3aBmAJkA38B7ASWKS17o+z+IuIn/M3UPxrieL5mzAiD3wO3A/8BEAptdj779uALwLPAv8L+FNgh9b6137P/VRrfav3eT8AHsQ4aWeScOIHeENr/b9nLtxJhBy/1vpN4E3v83YB/29GIzcI5/1/HPi11vpVpdTfK6VqtNYHZjR6i/Frrb/tPb4U+Get9RWl1BCwCZj4NzWThBN/f7ycv1PED1E8fxPGrtFaD2utu/3uKgaatNYe4CDwJe/99wC3KaU+UEp923yu3/PSgOMzEbM/4cTv5b8ppfYopZ5gFohA/OaJkaK1PjMjQfsRZvylwCHvzweB22ciZn9sxG/yh8B/ep/7uda6Z2YiDUyY8cfT+Wvii99L1M7fhBH5ALQCyvv1eguQ6b2/EDjgve9RpVQBxgPvUUo1ApsB18yHOwk78WuMr4B3AfcopdbPQrwTsfX+e3kI+NWMRjk1duL/FLjTe3wLcNMMxxqIqeI3iaX3OhC24o+j89fEP/6onr8JK/Ja60sYPt17wL1Ai/fQVaBOa+0G9uH1x7TWv9NaV2G88d+evOLMYid+rXW/1vq61vo68DaGxzer2H3/vfwB8F8zGedU2Iz/F0CFUur3GP7r+ZmPeDzTxG9aBZ9rrS/OTnTBsRt/HJ2/k+KP9vmbsCIPoLX+V631Jgy/9wPv3R8B67w/rwVOez9tTXow/LVZx0b86X5Pu43YyGQsxw+glMpllqyaqbAav9Z6QGv9Ta31Fu/978xspIGZIn4wPkz/M+CTYgir8cfZ+QuT44/q+ZtIF15RSu3AOAHLvVex7wFygDPAn3gf9gLwmlLqOeB9rfUppdQDSqknAQ/wGfDfZz76sOL/H0qpxwE38JHWevcshB9y/N77Z90+COP9Xwe8DIwC/6a1Pj3z0VuOH4z3+gG/592EITprgbeVUi9qrX87Y4GPxRFS/BgWR7ycvzA5/oejef5Kq2FBEIQEJqHtGkEQhBsdEXlBEIQERkReEAQhgRGRFwRBSGBE5AVBEBKYhCqhFISp8G5AMUsbv6+1/ivv/a8C3wTQWjtCXHsV8DDwgdb6A+99rwFfB76otdbhxC4I4SAiL9yIbFNKPQ/MxxDncFkF/KX35w8isJ4gRAypkxduCPwy+VMY7YzvBJYBfw9cAvIx7MtngMcweo1oYLvWusnb3fAvMVoYbAYyMLpSNjD2DcHkDmAbRib/EkYzqiTgUa31nuj8hoIQGPHkhRuNZqAew6L5JkZb46veY98A/go4giH2XwR+rZRy+j3/S8DfAguBHwPdGLtdwdix+8cYDctMbgX+CSgAfhDx30YQgiAiL9yI/DNGdr0R+Be/++/z3j6ptf4pRn/1UqDM7zF/7R1ocgpY6h1O8ZH32DGt9esTGn/9QGv9PDAELI34byIIQRCRF25EXgdGgE6MaT4T8Uy49eey99bN2Pkznefp//g59sIUhPARkRduOLTWvRhWzbe11qN+h37jvf1rpdSfYjSRagVOBFnyivf2S0qpP1JKpUU0YEEIA6muEW5ItNZvBLj7NYwLsI9hXJhtwLjwOuwd0TYVe4GdGBOh7sQYLCIIMYFU1wiCICQwYtcIgiAkMCLygiAICYyIvCAIQgIjIi8IgpDAiMgLgiAkMCLygiAICYyIvCAIQgLz/wEQ96meTtSDuwAAAABJRU5ErkJggg==\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "training, validation = series.split_before(pd.Timestamp('1973-01-01'))\n",
    "\n",
    "model = ExponentialSmoothing()\n",
    "model.fit(training)\n",
    "forecast = model.predict(36)\n",
    "\n",
    "plt.title('MAPE = {:.2f}%'.format(mape(forecast, validation)))\n",
    "series.plot(label='actual')\n",
    "forecast.plot(label='forecast')\n",
    "plt.legend()\n"
   ]
  },
  {
   "source": [
    "To take this fact into account and achieve better performance, we could instead:\n",
    "\n",
    "1. Transform the time series to represent the average daily production of milk for each month (instead of the total production per month)\n",
    "2. Make a forecast\n",
    "3. Inverse the transformation\n",
    "\n",
    "Let's see how this would be implemented using `InvertibleMapper` and `pd.timestamp.days_in_month`\n",
    "\n",
    "(Idea taken from [\"Forecasting: principles and Practice\"](https://otexts.com/fpp2/transformations.html) by Hyndman and Athanasopoulos)"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "To transform the time series, we have to divide a monthly value (the data point) by the number of days in the month given by the value's corresponding timestamp.\n",
    "\n",
    "`map()` (and thus `Mapper` / `InvertibleMapper`) makes this convenient by allowing to apply a transformation function which uses both the value and its timestamp to compute the new value: `f(timestamp, value) = new_value`"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# Transform the time series\n",
    "toDailyAverage = InvertibleMapper(fn=lambda timestamp, x: x/timestamp.days_in_month,\n",
    "                               inverse_fn=lambda timestamp, x: x*timestamp.days_in_month)\n",
    "\n",
    "dailyAverage = toDailyAverage.transform(series)\n",
    "\n",
    "dailyAverage.plot()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x140358820>"
      ]
     },
     "metadata": {},
     "execution_count": 12
    },
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# Make a forecast\n",
    "dailyavg_train, dailyavg_val = dailyAverage.split_after(pd.Timestamp('1973-01-01'))\n",
    "\n",
    "model = ExponentialSmoothing()\n",
    "model.fit(dailyavg_train)\n",
    "dailyavg_forecast = model.predict(36)\n",
    "\n",
    "plt.title('MAPE = {:.2f}%'.format(mape(dailyavg_forecast, dailyavg_val)))\n",
    "dailyAverage.plot()\n",
    "dailyavg_forecast.plot()\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Inverse the transformation\n",
    "forecast = toDailyAverage.inverse_transform(dailyavg_forecast)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x132a2a040>"
      ]
     },
     "metadata": {},
     "execution_count": 14
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.title('MAPE = {:.2f}%'.format(mape(forecast, validation)))\n",
    "series.plot(label='actual')\n",
    "forecast.plot(label='forecast')\n",
    "plt.legend()"
   ]
  },
  {
   "source": [
    "## Chaining transformations : introducing `Pipeline`\n",
    "\n",
    "Now suppose that we both want to apply the above transformation (daily averaging), and rescale the dataset between 0 and 1 to use a Neural Network based forecasting model. Instead of applying these two transformations separately, and then inversing them separately, we can use a `Pipeline`.\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "pipeline = Pipeline([toDailyAverage, scaler])\n",
    "transformed = pipeline.fit_transform(training)\n",
    "transformed.plot()"
   ]
  },
  {
   "source": [
    "If all transformations in the pipeline are invertible, the `Pipeline` object is too."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "back = pipeline.inverse_transform(transformed)\n",
    "back.plot()"
   ]
  }
 ]
}